Abstract Accelerating farmers’ adoption of postharvest green technologies is critical for achieving sustainable agricultural transitions, yet the behavioral and cognitive foundations underlying farmers’ adoption decisions remain insufficiently understood. Anchored in capital structure theory, the theory of planned behavior (TPB), and institutional fit theory, this study develops an integrated analytical framework linking structural conditions, cognitive mediation, and institutional adaptation to explain adoption behavior in the context of air-source heat pump drying technology. Drawing on primary survey data collected from 439 moderate-scale farmers in Gutian County, Ningde City, Fujian Province, China, between July and September 2023, a binary logistic regression model and causal mediation effect analysis (CMA) were employed to test the proposed hypotheses. The results reveal that economic capital exerts the strongest positive effect on adoption (β = 0.645, P .01), followed by cultural capital (β = 0.568, P .01) and social capital (β = 0.501, P .01), which jointly constitute the structural foundation of adoption propensity. Perceived value—comprising economic, functional, ecological, and social dimensions—serves as a significant cognitive mediator between capital endowment and behavioral intention, with economic and functional value dimensions being the most influential. Furthermore, incentive-based and constraint-based policy instruments significantly moderate the relationship between perceived value and adoption likelihood, particularly when policy incentives align with farmers’ cognitive appraisals. The findings demonstrate that green technology adoption is neither purely resource-driven nor policy-imposed, but embedded within a dynamic interplay of capital asymmetries, cognitive processing, and institutional design. This study contributes to the literature by clarifying the microlevel behavioral mechanisms of sustainable technology diffusion and providing practical insights into designing policy instruments aligned with farmers’ decision logic in postharvest green processing contexts.
Chen et al. (2026) studied this question.